Hugging Face Unveils Microduck: A $399 Open-Source 25 cm Biped You Train with Reinforcement LearningMichal Sutter writes about Pollen Robotics, a Bordeaux-based team at Hugging Face, which has opened pre-orders for Microduck, a 25 cm bipedal robot priced at $399. Unlike most robotics launches that rely on demo videos, Microduck ships with its full training loop — every movement (walking, sitting, kicking, roller-skating, self-recovery) is a neural policy trained in a physics simulator and exported to hardware. The robot carries 15 motors, a camera, LiDAR, two IMUs, and a Rockchip RK3566, with policies trained via PPO in MuJoCo Warp in roughly one to two hours on a CUDA GPU.
- Sim-to-real hinges on a BAM actuator model (voltage control law, back-EMF, Coulomb/Stribeck/load-dependent friction) plus randomization of battery voltage, command delay, and ±1° backlash per joint
- Every policy shares a 61-dimensional actor observation (48 proprioception + twist, head pose, body pose commands), enabling hot-swap between walk, recover, and trick policies mid-run
- Software is Apache-2.0, but mechanical and electronic design files are not open
- The robot generates a unique audio identity on first wake that persists permanently; it does not speak in a linguistic sense
- Pre-orders opened August 27, 2026, with deliveries targeted before Christmas
- Sim-to-real hinges on a BAM actuator model (voltage control law, back-EMF, Coulomb/Stribeck/load-dependent friction) plus randomization of battery voltage, command delay, and ±1° backlash per joint
- Every policy shares a 61-dimensional actor observation (48 proprioception + twist, head pose, body pose commands), enabling hot-swap between walk, recover, and trick policies mid-run
- Software is Apache-2.0, but mechanical and electronic design files are not open
- The robot generates a unique audio identity on first wake that persists permanently; it does not speak in a linguistic sense
- Pre-orders opened August 27, 2026, with deliveries targeted before Christmas
Erik Kristensen and Napalys Klicius write about four changes to the GitHub Copilot harness that reduce token costs without sacrificing task quality. The central insight is that optimizing individual tool calls is the wrong metric — a shorter response can cost more overall if it forces the agent to rerun commands or reread output. The four changes are: selectively compressing repetitive build/test/install output while preserving source-like content, removing unused line-number prefixes from file reads, halving the task-tool prompt via a meta-prompting loop, and batching background completion notifications so results arrive without an extra retrieval turn. Each was validated through offline agentic benchmarks and controlled online A/B experiments before shipping.
- RTK (Rust Token Killer) was evaluated and found to increase end-to-end cost despite shortening individual responses, because the agent reopened or reran commands to recover omitted details.
- The prompt compression initially caused a regression that offline tests missed: cautious parallelism guidance was rewritten into a hard scheduling policy, serializing independent agents. The fix was a single sentence: "Independent agents can run in parallel; consider side effects."
- A tighter file-tool instruction set that worked in Copilot code review actually increased cost in Copilot CLI, illustrating that evidence is local to the workload.
- The changes ship across all Copilot products sharing the same harness (CLI, app, code review); code review separately saw ~20% cost reduction from a prior migration to shared file tools.
- RTK (Rust Token Killer) was evaluated and found to increase end-to-end cost despite shortening individual responses, because the agent reopened or reran commands to recover omitted details.
- The prompt compression initially caused a regression that offline tests missed: cautious parallelism guidance was rewritten into a hard scheduling policy, serializing independent agents. The fix was a single sentence: "Independent agents can run in parallel; consider side effects."
- A tighter file-tool instruction set that worked in Copilot code review actually increased cost in Copilot CLI, illustrating that evidence is local to the workload.
- The changes ship across all Copilot products sharing the same harness (CLI, app, code review); code review separately saw ~20% cost reduction from a prior migration to shared file tools.
NPC-Worldwide (primary contributor cagostino) presents npcsh, a composable multi-agent shell that interprets both bash commands and natural language within a single interactive interface. Built primarily in Rust with a Python backend (npcpy) for the LLM inference loop, it lets users delegate tasks to named agents, define custom "Jinxes" (Jinja Execution templates) for tool-use and skills, and works with any model provider LiteLLM supports. A 100-task benchmark suite scores how well various models can drive the shell, with results ranging from 23% (Qwen3.5 0.8b) to 97% (Qwen3.5 35b, Ornith 35b, Kimi K2.7-Code 1t).
- Agent definitions support three interchangeable formats: .npc YAML files, agents.md markdown, and agents/ directories with per-agent .md files
- The Python backend (npcpy) is explicitly temporary and slated for replacement by a Rust-native runner (npcrs)
- The project references an arxiv paper on "ALARA for Agents: Least-Privilege Context Engineering Through Portable Composable Multi-Agent Teams"
- Supports local model runtimes including Ollama, LM Studio, and MLX (Apple Silicon)
- Currently at v2.1.16 with 128 releases, 473 stars, and 854 commits
- Agent definitions support three interchangeable formats: .npc YAML files, agents.md markdown, and agents/ directories with per-agent .md files
- The Python backend (npcpy) is explicitly temporary and slated for replacement by a Rust-native runner (npcrs)
- The project references an arxiv paper on "ALARA for Agents: Least-Privilege Context Engineering Through Portable Composable Multi-Agent Teams"
- Supports local model runtimes including Ollama, LM Studio, and MLX (Apple Silicon)
- Currently at v2.1.16 with 128 releases, 473 stars, and 854 commits
Anurag Singh writes about combining Claude Code's Auto mode with deny rules and ask rules to eliminate the need to manually approve every command. The setup lets Claude Code work uninterrupted in the background while hard-blocking destructive commands like force-pushes and rm -rf, and optionally prompting on risky-but-acceptable actions like git push.
- Deny rules are enforced by Claude Code itself rather than being instructions to the model, so they hold even in bypassPermissions mode
- A deny list can never be exhaustive; Claude could accomplish the same destructive action through a different tool (e.g., Python instead of rm)
- The built-in sandbox is a stronger safety net than any deny list, though it becomes tedious for projects depending on local databases, Docker, or private registries
- Permission rules are evaluated in fixed priority order: deny first, then ask, then allow
- Deny rules are enforced by Claude Code itself rather than being instructions to the model, so they hold even in bypassPermissions mode
- A deny list can never be exhaustive; Claude could accomplish the same destructive action through a different tool (e.g., Python instead of rm)
- The built-in sandbox is a stronger safety net than any deny list, though it becomes tedious for projects depending on local databases, Docker, or private registries
- Permission rules are evaluated in fixed priority order: deny first, then ask, then allow
Al Williams writes about the surprising continued availability of Reverse Polish Notation calculators in 2026, despite RPN losing the mass market to algebraic entry decades ago. RPN uses a stack-based system where operators follow their operands, eliminating the need for parentheses or an equals key. Today's options range from the still-sold HP-12C to SwissMicros' modern enthusiast machines, DIY builds on ATmega and ESP32 hardware, and a thriving ecosystem of phone and desktop apps.
- The first RPN calculator was the Friden EC-130 (1964), a 44-pound desktop unit at $2,150 whose CRT displayed all four stack levels simultaneously
- SwissMicros' R47, at ~$300, features an 8-level stack, built-in electrical engineering functions, and firmware still officially in beta
- HP released a native Linux build of HP Prime as a 2019 AppImage, but it predates the Python programming addition; the only way to get Python on Linux is running an older Windows version under Wine with Soda 9 in Bottles, and you must not let it update
- Alex Garza's PAXER kits reproduce HP-15C/16C/41C on ATmega328 for under $100, adding continuous memory, backlight, and real-time clock
- The 10LC turns a ~$50 ESP32-based M5Stack Cardputer into a pocket RPN calculator
- Free42, a clean-room HP-42S implementation by Thomas Okken, runs on Android, iOS, Windows, macOS, and Linux
- The first RPN calculator was the Friden EC-130 (1964), a 44-pound desktop unit at $2,150 whose CRT displayed all four stack levels simultaneously
- SwissMicros' R47, at ~$300, features an 8-level stack, built-in electrical engineering functions, and firmware still officially in beta
- HP released a native Linux build of HP Prime as a 2019 AppImage, but it predates the Python programming addition; the only way to get Python on Linux is running an older Windows version under Wine with Soda 9 in Bottles, and you must not let it update
- Alex Garza's PAXER kits reproduce HP-15C/16C/41C on ATmega328 for under $100, adding continuous memory, backlight, and real-time clock
- The 10LC turns a ~$50 ESP32-based M5Stack Cardputer into a pocket RPN calculator
- Free42, a clean-room HP-42S implementation by Thomas Okken, runs on Android, iOS, Windows, macOS, and Linux
Tom Angell writes about a NORML analysis of FBI Uniform Crime Reporting data showing that ten states **Texas, North Carolina, Wisconsin, Georgia, Tennessee, Pennsylvania, South Carolina, Indiana, Louisiana, and Alabama** accounted for roughly 60 percent of the 200,960 marijuana possession arrests nationwide in 2025, with Texas alone responsible for nearly 29,000. The data reveals that 92 percent of all cannabis incidents stem from possession, and marijuana arrests now constitute a quarter of all drug arrests, making it the most-enforced single substance.
- Louisiana had the highest share of total drug arrests from cannabis possession at 62 percent, and its governor recently signed a law jailing people up to one year for smoking within 2,000 feet of school property
- About 13 percent of state and local law enforcement agencies did not contribute data for 2025
- The FBI report contains internal inconsistencies, presenting three different totals for "Drug/Narcotic Offenses" (1,492,005; 1,656,774; and 1,958,377)
- Even in legalized jurisdictions arrests persist: Illinois reported 5,731 possession arrests and Washington, D.C. reported 1,336
- NORML Deputy Director Paul Armentano noted that in nearly half the states, low-level marijuana arrests have "fallen nearly to zero"
- Louisiana had the highest share of total drug arrests from cannabis possession at 62 percent, and its governor recently signed a law jailing people up to one year for smoking within 2,000 feet of school property
- About 13 percent of state and local law enforcement agencies did not contribute data for 2025
- The FBI report contains internal inconsistencies, presenting three different totals for "Drug/Narcotic Offenses" (1,492,005; 1,656,774; and 1,958,377)
- Even in legalized jurisdictions arrests persist: Illinois reported 5,731 possession arrests and Washington, D.C. reported 1,336
- NORML Deputy Director Paul Armentano noted that in nearly half the states, low-level marijuana arrests have "fallen nearly to zero"
Geiping writes about a language model architecture that scales test-time compute by iterating a recurrent block to reason implicitly in latent space, unrolling to arbitrary depth at inference time rather than generating more tokens. Unlike chain-of-thought methods, this approach needs no specialized training data, operates with small context windows, and can represent reasoning that is difficult to express linguistically. The authors scale a proof-of-concept to 3.5 billion parameters trained on 800 billion tokens, showing dramatic benchmark improvements at computation loads equivalent to 50 billion parameters.
- Hugging Face "huginn-0125"
- GitHub com/seal-rg/huginn-0125
- Hugging Face "huginn-0125"
- GitHub com/seal-rg/huginn-0125
Benjamin Marie writes about a comprehensive benchmark of 15 GGUF quantizations of Qwen3.8 27B, ranging from Q4_K_XL down to IQ1_M, evaluated using over 150 million tokens generated across roughly 8 days on an NVIDIA RTX Pro 6000. Using 950 prompts subsampled from MMLU-Pro, LiveCodeBench, and GPQA Diamond, he measures both accuracy and token efficiency to identify the lowest quantization level that retains at least 95% of the original BF16 model's performance.
| Chart pt | Quantization | Provider | GGUF file | Size (GB) | Accuracy recovery vs BF16 | Tokens generated | ≥ 95% threshold? |
|---|---|---|---|---|---|---|---|
| 1 | IQ3_XXS | bartowski | Qwen3.8-27B-IQ3_XXS.gguf | 12.39 | 97.7% | 11.54M | Yes |
| 2 | IQ4_XS | bartowski | Qwen3.8-27B-IQ4_XS.gguf | 15.33 | 99.1% | 10.18M | Yes |
| 3 | IQ2_S (AD) | AtomicChat | Qwen3.8-27B-AD-IQ2_S.gguf | 10.85 | 95.9% | 12.24M | Yes |
| 4 | IQ3_S (AD) | AtomicChat | Qwen3.8-27B-AD-IQ3_S.gguf | 13.60 | 101.1% | 10.48M | Yes* |
| 5 | Q4_K_M (AD) | AtomicChat | Qwen3.8-27B-AD-Q4_K_M.gguf | 16.84 | 99.9% | 9.99M | Yes |
| 6 | IQ2_S (GSQ-RCO) | ISTA-DASLab | Qwen3.8-27B-GSQ-RCO-IQ2_S.gguf | 9.26 | 92.2% | 11.81M | **No** |
| 7 | IQ3_XXS (GSQ-RCO) | ISTA-DASLab | Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf | 10.09 | 96.5% | 10.87M | Yes |
| 8 | Ridge 3.7 bpw | empero-ai | Qwen3.8-27B-Ridge-3.7bpw.gguf | 12.26 | 97.4% | 10.29M | Yes |
| 9 | IQ1_M (UD) | unsloth | Qwen3.8-27B-UD-IQ1_M.gguf | 6.73 | 53.4% | 15.41M | **No** |
| 10 | IQ2_XXS (UD) | unsloth | Qwen3.8-27B-UD-IQ2_XXS.gguf | 7.27 | 74.3% | 11.96M | **No** |
| 11 | IQ3_XXS (UD) | unsloth | Qwen3.8-27B-UD-IQ3_XXS.gguf | 10.58 | 95.5% | 12.27M | Yes |
| 12 | Q2_K_XL (UD) | unsloth | Qwen3.8-27B-UD-Q2_K_XL.gguf | 9.48 | 96.0% | 11.89M | Yes |
| 13 | Q3_K_XL (UD) | unsloth | Qwen3.8-27B-UD-Q3_K_XL.gguf | 12.80 | 100.0% | 9.89M | Yes |
| 14 | Q4_K_XL (UD) | unsloth | Qwen3.8-27B-UD-Q4_K_XL.gguf | 17.21 | 101.0% | 9.47M | Yes* |
| 15 | Q4_K_XL abliterated (Huihui UD) | huihui-ai | Huihui-Qwen3.8-27B-abliterated-UD-Q4_K_XL.gguf | 17.03 | 98.8% | 9.93M | Yes |
`* = above 100% BF16 (sampling variance, not a real gain); below-threshold points are 6, 9, and 10.`
| Chart pt | Quantization | Provider | GGUF file | Size (GB) | Accuracy recovery vs BF16 | Tokens generated | ≥ 95% threshold? |
|---|---|---|---|---|---|---|---|
| 1 | IQ3_XXS | bartowski | Qwen3.8-27B-IQ3_XXS.gguf | 12.39 | 97.7% | 11.54M | Yes |
| 2 | IQ4_XS | bartowski | Qwen3.8-27B-IQ4_XS.gguf | 15.33 | 99.1% | 10.18M | Yes |
| 3 | IQ2_S (AD) | AtomicChat | Qwen3.8-27B-AD-IQ2_S.gguf | 10.85 | 95.9% | 12.24M | Yes |
| 4 | IQ3_S (AD) | AtomicChat | Qwen3.8-27B-AD-IQ3_S.gguf | 13.60 | 101.1% | 10.48M | Yes* |
| 5 | Q4_K_M (AD) | AtomicChat | Qwen3.8-27B-AD-Q4_K_M.gguf | 16.84 | 99.9% | 9.99M | Yes |
| 6 | IQ2_S (GSQ-RCO) | ISTA-DASLab | Qwen3.8-27B-GSQ-RCO-IQ2_S.gguf | 9.26 | 92.2% | 11.81M | **No** |
| 7 | IQ3_XXS (GSQ-RCO) | ISTA-DASLab | Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf | 10.09 | 96.5% | 10.87M | Yes |
| 8 | Ridge 3.7 bpw | empero-ai | Qwen3.8-27B-Ridge-3.7bpw.gguf | 12.26 | 97.4% | 10.29M | Yes |
| 9 | IQ1_M (UD) | unsloth | Qwen3.8-27B-UD-IQ1_M.gguf | 6.73 | 53.4% | 15.41M | **No** |
| 10 | IQ2_XXS (UD) | unsloth | Qwen3.8-27B-UD-IQ2_XXS.gguf | 7.27 | 74.3% | 11.96M | **No** |
| 11 | IQ3_XXS (UD) | unsloth | Qwen3.8-27B-UD-IQ3_XXS.gguf | 10.58 | 95.5% | 12.27M | Yes |
| 12 | Q2_K_XL (UD) | unsloth | Qwen3.8-27B-UD-Q2_K_XL.gguf | 9.48 | 96.0% | 11.89M | Yes |
| 13 | Q3_K_XL (UD) | unsloth | Qwen3.8-27B-UD-Q3_K_XL.gguf | 12.80 | 100.0% | 9.89M | Yes |
| 14 | Q4_K_XL (UD) | unsloth | Qwen3.8-27B-UD-Q4_K_XL.gguf | 17.21 | 101.0% | 9.47M | Yes* |
| 15 | Q4_K_XL abliterated (Huihui UD) | huihui-ai | Huihui-Qwen3.8-27B-abliterated-UD-Q4_K_XL.gguf | 17.03 | 98.8% | 9.93M | Yes |
`* = above 100% BF16 (sampling variance, not a real gain); below-threshold points are 6, 9, and 10.`
Meredith Shubel writes that Vercel published `design.md`, a public prompt file that cut agent-generated design failures by 57% across 200+ agent runs, though none of the six tested pages was ship-ready. The system has three layers: a prompt encoding design judgment, a public stylesheet for mechanical layout rules, and an evaluation loop that converts human feedback into deterministic checks. A Slack-based agent (`design-agent`) consolidates weekly feedback from GitHub and Figma into proposed guidance updates.
- The comparison test used Codex with GPT-5.5: 39 failure instances with `design.md` versus 91 without.
- Vercel's first attempt to port its internal "product design" skill to a public prompt failed because subjective design language was interpreted differently by each model.
- Recurring complaint counts are tracked over time; if a fix doesn't reduce its count, the fix is flagged for refinement.
- The comparison test used Codex with GPT-5.5: 39 failure instances with `design.md` versus 91 without.
- Vercel's first attempt to port its internal "product design" skill to a public prompt failed because subjective design language was interpreted differently by each model.
- Recurring complaint counts are tracked over time; if a fix doesn't reduce its count, the fix is flagged for refinement.
@githubprojects writes about Quivr-core, a Python package extracted from Quivr.com's production retrieval-augmented generation pipeline that lets developers get a working system in five lines of code. It centers on a `Brain` class that ingests files and answers questions, with a YAML-configurable workflow (filter history → rewrite → retrieve → generate) that keeps the pipeline inspectable rather than a black box. It supports OpenAI, Anthropic, Mistral, and local Ollama models, and handles PDFs, Markdown, and TXT files out of the box.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
- Integrates with Megaparse for more sophisticated document ingestion without switching frameworks.
- The opinionated philosophy is the core differentiator: sensible defaults over an infinite configuration surface.
